Overview

Dataset statistics

Number of variables15
Number of observations31595
Missing cells72
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory120.0 B

Variable types

DateTime2
Numeric8
Categorical5

Warnings

Previsión diaria D+1 fotovoltaica has 13393 (42.4%) zeros Zeros
Weekday has 4512 (14.3%) zeros Zeros
Hour has 1317 (4.2%) zeros Zeros

Reproduction

Analysis started2022-08-11 17:08:29.965775
Analysis finished2022-08-11 17:08:45.441704
Duration15.48 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Distinct31592
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size247.0 KiB
Minimum2019-01-01 00:00:00
Maximum2022-08-09 11:00:00
2022-08-11T19:08:45.585740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:45.760745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Previsión diaria D+1 demanda
Real number (ℝ≥0)

Distinct15587
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27796.52349
Minimum14170
Maximum41773
Zeros0
Zeros (%)0.0%
Memory size247.0 KiB
2022-08-11T19:08:45.948711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum14170
5-th percentile20833
Q124100.5
median27837
Q331234
95-th percentile35065.9
Maximum41773
Range27603
Interquartile range (IQR)7133.5

Descriptive statistics

Standard deviation4494.007319
Coefficient of variation (CV)0.1616751577
Kurtosis-0.7678018411
Mean27796.52349
Median Absolute Deviation (MAD)3573
Skewness0.07802011851
Sum878231159.6
Variance20196101.78
MonotocityNot monotonic
2022-08-11T19:08:46.112752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
309999
 
< 0.1%
314469
 
< 0.1%
311269
 
< 0.1%
311899
 
< 0.1%
245539
 
< 0.1%
235228
 
< 0.1%
236218
 
< 0.1%
311518
 
< 0.1%
274398
 
< 0.1%
248618
 
< 0.1%
Other values (15577)31510
99.7%
ValueCountFrequency (%)
141701
< 0.1%
143291
< 0.1%
149271
< 0.1%
149751
< 0.1%
151761
< 0.1%
155291
< 0.1%
157241
< 0.1%
162311
< 0.1%
162461
< 0.1%
163031
< 0.1%
ValueCountFrequency (%)
417731
< 0.1%
409871
< 0.1%
405991
< 0.1%
405891
< 0.1%
405141
< 0.1%
404591
< 0.1%
404062
< 0.1%
403071
< 0.1%
402621
< 0.1%
401681
< 0.1%

Previsión diaria D+1 fotovoltaica
Real number (ℝ≥0)

ZEROS

Distinct15359
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.770945
Minimum0
Maximum11892
Zeros13393
Zeros (%)42.4%
Memory size247.0 KiB
2022-08-11T19:08:46.350722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median144
Q33419.6
95-th percentile7956.22
Maximum11892
Range11892
Interquartile range (IQR)3419.6

Descriptive statistics

Standard deviation2782.867091
Coefficient of variation (CV)1.399289897
Kurtosis0.9147956786
Mean1988.770945
Median Absolute Deviation (MAD)144
Skewness1.359970669
Sum62835218
Variance7744349.247
MonotocityNot monotonic
2022-08-11T19:08:46.555732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013393
42.4%
0.175
 
0.2%
0.358
 
0.2%
0.255
 
0.2%
0.433
 
0.1%
0.631
 
0.1%
1.129
 
0.1%
0.825
 
0.1%
0.923
 
0.1%
0.523
 
0.1%
Other values (15349)17850
56.5%
ValueCountFrequency (%)
013393
42.4%
0.175
 
0.2%
0.255
 
0.2%
0.358
 
0.2%
0.433
 
0.1%
0.523
 
0.1%
0.631
 
0.1%
0.715
 
< 0.1%
0.825
 
0.1%
0.923
 
0.1%
ValueCountFrequency (%)
118921
< 0.1%
11874.91
< 0.1%
11857.31
< 0.1%
11823.51
< 0.1%
11811.21
< 0.1%
11777.61
< 0.1%
117731
< 0.1%
11760.41
< 0.1%
11759.41
< 0.1%
11754.61
< 0.1%

Previsión diaria D+1 eólica
Real number (ℝ≥0)

Distinct13196
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6315.756224
Minimum348
Maximum19109
Zeros0
Zeros (%)0.0%
Memory size247.0 KiB
2022-08-11T19:08:46.765776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum348
5-th percentile1743.7
Q13542
median5597
Q38471.5
95-th percentile13467.3
Maximum19109
Range18761
Interquartile range (IQR)4929.5

Descriptive statistics

Standard deviation3569.313064
Coefficient of variation (CV)0.5651442104
Kurtosis0.1266514708
Mean6315.756224
Median Absolute Deviation (MAD)2331
Skewness0.8118129128
Sum199546317.9
Variance12739995.75
MonotocityNot monotonic
2022-08-11T19:08:46.937775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510613
 
< 0.1%
369011
 
< 0.1%
329311
 
< 0.1%
380711
 
< 0.1%
339311
 
< 0.1%
355011
 
< 0.1%
289210
 
< 0.1%
396510
 
< 0.1%
559710
 
< 0.1%
604610
 
< 0.1%
Other values (13186)31487
99.7%
ValueCountFrequency (%)
3481
< 0.1%
3801
< 0.1%
3861
< 0.1%
4011
< 0.1%
4031
< 0.1%
4271
< 0.1%
4291
< 0.1%
4321
< 0.1%
4451
< 0.1%
4531
< 0.1%
ValueCountFrequency (%)
191091
< 0.1%
189651
< 0.1%
189211
< 0.1%
187841
< 0.1%
187121
< 0.1%
186351
< 0.1%
186171
< 0.1%
185731
< 0.1%
185671
< 0.1%
184911
< 0.1%

Precio mercado SPOT Diario
Real number (ℝ≥0)

Distinct11504
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.2803874
Minimum0.01
Maximum700
Zeros0
Zeros (%)0.0%
Memory size247.0 KiB
2022-08-11T19:08:47.132774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile19
Q138.5
median52.17
Q3115.965
95-th percentile239.551
Maximum700
Range699.99
Interquartile range (IQR)77.465

Descriptive statistics

Standard deviation75.36899787
Coefficient of variation (CV)0.8735356915
Kurtosis3.123851958
Mean86.2803874
Median Absolute Deviation (MAD)20.83
Skewness1.663693752
Sum2726028.84
Variance5680.48584
MonotocityNot monotonic
2022-08-11T19:08:47.320753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.95108
 
0.3%
5092
 
0.3%
4088
 
0.3%
3083
 
0.3%
4568
 
0.2%
2561
 
0.2%
3559
 
0.2%
20051
 
0.2%
5550
 
0.2%
4950
 
0.2%
Other values (11494)30885
97.8%
ValueCountFrequency (%)
0.012
 
< 0.1%
0.032
 
< 0.1%
0.13
 
< 0.1%
0.151
 
< 0.1%
0.1625
0.1%
0.23
 
< 0.1%
0.321
 
< 0.1%
0.443
 
< 0.1%
0.52
 
< 0.1%
0.513
 
< 0.1%
ValueCountFrequency (%)
7001
< 0.1%
654.911
< 0.1%
6512
< 0.1%
6501
< 0.1%
6451
< 0.1%
6051
< 0.1%
603.081
< 0.1%
6011
< 0.1%
6002
< 0.1%
5821
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
-1.0
16178 
1.0
15417 

Length

Max length4
Median length4
Mean length3.512043045
Min length3

Characters and Unicode

Total characters110963
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0
ValueCountFrequency (%)
-1.016178
51.2%
1.015417
48.8%
2022-08-11T19:08:47.740782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-08-11T19:08:47.881766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031595
100.0%

Most occurring characters

ValueCountFrequency (%)
131595
28.5%
.31595
28.5%
031595
28.5%
-16178
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number63190
56.9%
Other Punctuation31595
28.5%
Dash Punctuation16178
 
14.6%

Most frequent character per category

ValueCountFrequency (%)
131595
50.0%
031595
50.0%
ValueCountFrequency (%)
-16178
100.0%
ValueCountFrequency (%)
.31595
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common110963
100.0%

Most frequent character per script

ValueCountFrequency (%)
131595
28.5%
.31595
28.5%
031595
28.5%
-16178
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII110963
100.0%

Most frequent character per block

ValueCountFrequency (%)
131595
28.5%
.31595
28.5%
031595
28.5%
-16178
14.6%

Signo_lag_24h
Categorical

Distinct2
Distinct (%)< 0.1%
Missing24
Missing (%)0.1%
Memory size1.8 MiB
-1.0
16172 
1.0
15399 

Length

Max length4
Median length4
Mean length3.512242248
Min length3

Characters and Unicode

Total characters110885
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0
ValueCountFrequency (%)
-1.016172
51.2%
1.015399
48.7%
(Missing)24
 
0.1%
2022-08-11T19:08:48.261776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-08-11T19:08:48.390779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031571
100.0%

Most occurring characters

ValueCountFrequency (%)
131571
28.5%
.31571
28.5%
031571
28.5%
-16172
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number63142
56.9%
Other Punctuation31571
28.5%
Dash Punctuation16172
 
14.6%

Most frequent character per category

ValueCountFrequency (%)
131571
50.0%
031571
50.0%
ValueCountFrequency (%)
-16172
100.0%
ValueCountFrequency (%)
.31571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common110885
100.0%

Most frequent character per script

ValueCountFrequency (%)
131571
28.5%
.31571
28.5%
031571
28.5%
-16172
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII110885
100.0%

Most frequent character per block

ValueCountFrequency (%)
131571
28.5%
.31571
28.5%
031571
28.5%
-16172
14.6%

Signo_lag_48h
Categorical

Distinct2
Distinct (%)< 0.1%
Missing48
Missing (%)0.2%
Memory size1.8 MiB
-1.0
16165 
1.0
15382 

Length

Max length4
Median length4
Mean length3.512410055
Min length3

Characters and Unicode

Total characters110806
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0
ValueCountFrequency (%)
-1.016165
51.2%
1.015382
48.7%
(Missing)48
 
0.2%
2022-08-11T19:08:48.701792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-08-11T19:08:48.812827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031547
100.0%

Most occurring characters

ValueCountFrequency (%)
131547
28.5%
.31547
28.5%
031547
28.5%
-16165
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number63094
56.9%
Other Punctuation31547
28.5%
Dash Punctuation16165
 
14.6%

Most frequent character per category

ValueCountFrequency (%)
131547
50.0%
031547
50.0%
ValueCountFrequency (%)
-16165
100.0%
ValueCountFrequency (%)
.31547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common110806
100.0%

Most frequent character per script

ValueCountFrequency (%)
131547
28.5%
.31547
28.5%
031547
28.5%
-16165
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII110806
100.0%

Most frequent character per block

ValueCountFrequency (%)
131547
28.5%
.31547
28.5%
031547
28.5%
-16165
14.6%

Holiday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
30875 
1
 
720

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31595
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
030875
97.7%
1720
 
2.3%
2022-08-11T19:08:49.133801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-08-11T19:08:49.243836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
030875
97.7%
1720
 
2.3%

Most occurring characters

ValueCountFrequency (%)
030875
97.7%
1720
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31595
100.0%

Most frequent character per category

ValueCountFrequency (%)
030875
97.7%
1720
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common31595
100.0%

Most frequent character per script

ValueCountFrequency (%)
030875
97.7%
1720
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII31595
100.0%

Most frequent character per block

ValueCountFrequency (%)
030875
97.7%
1720
 
2.3%

Year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2020
8784 
2019
8760 
2021
8760 
2022
5291 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters126380
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019
ValueCountFrequency (%)
20208784
27.8%
20198760
27.7%
20218760
27.7%
20225291
16.7%
2022-08-11T19:08:49.497843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-08-11T19:08:49.593812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
20208784
27.8%
20218760
27.7%
20198760
27.7%
20225291
16.7%

Most occurring characters

ValueCountFrequency (%)
259721
47.3%
040379
32.0%
117520
 
13.9%
98760
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126380
100.0%

Most frequent character per category

ValueCountFrequency (%)
259721
47.3%
040379
32.0%
117520
 
13.9%
98760
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common126380
100.0%

Most frequent character per script

ValueCountFrequency (%)
259721
47.3%
040379
32.0%
117520
 
13.9%
98760
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII126380
100.0%

Most frequent character per block

ValueCountFrequency (%)
259721
47.3%
040379
32.0%
117520
 
13.9%
98760
 
6.9%

Month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.129134357
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size247.0 KiB
2022-08-11T19:08:49.746819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.37924439
Coefficient of variation (CV)0.5513412161
Kurtosis-1.120129987
Mean6.129134357
Median Absolute Deviation (MAD)3
Skewness0.1511555164
Sum193650
Variance11.41929264
MonotocityNot monotonic
2022-08-11T19:08:49.928826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
72976
9.4%
52976
9.4%
12976
9.4%
32972
9.4%
62880
9.1%
42880
9.1%
22712
8.6%
82436
7.7%
102235
7.1%
122232
7.1%
Other values (2)4320
13.7%
ValueCountFrequency (%)
12976
9.4%
22712
8.6%
32972
9.4%
42880
9.1%
52976
9.4%
62880
9.1%
72976
9.4%
82436
7.7%
92160
6.8%
102235
7.1%
ValueCountFrequency (%)
122232
7.1%
112160
6.8%
102235
7.1%
92160
6.8%
82436
7.7%
72976
9.4%
62880
9.1%
52976
9.4%
42880
9.1%
32972
9.4%

Day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.64969141
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size247.0 KiB
2022-08-11T19:08:50.139827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8133257
Coefficient of variation (CV)0.5631629066
Kurtosis-1.199056187
Mean15.64969141
Median Absolute Deviation (MAD)8
Skewness0.01695488994
Sum494452
Variance77.6747099
MonotocityNot monotonic
2022-08-11T19:08:50.279862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
81056
 
3.3%
71056
 
3.3%
11056
 
3.3%
21056
 
3.3%
31056
 
3.3%
41056
 
3.3%
51056
 
3.3%
61056
 
3.3%
91044
 
3.3%
251033
 
3.3%
Other values (21)21070
66.7%
ValueCountFrequency (%)
11056
3.3%
21056
3.3%
31056
3.3%
41056
3.3%
51056
3.3%
61056
3.3%
71056
3.3%
81056
3.3%
91044
3.3%
101032
3.3%
ValueCountFrequency (%)
31600
1.9%
30936
3.0%
29959
3.0%
281031
3.3%
271032
3.3%
261032
3.3%
251033
3.3%
241032
3.3%
231032
3.3%
221032
3.3%

Weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.999145434
Minimum0
Maximum6
Zeros4512
Zeros (%)14.3%
Memory size247.0 KiB
2022-08-11T19:08:50.412870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.999991905
Coefficient of variation (CV)0.666853925
Kurtosis-1.250258297
Mean2.999145434
Median Absolute Deviation (MAD)2
Skewness0.000795255058
Sum94758
Variance3.999967618
MonotocityNot monotonic
2022-08-11T19:08:50.520841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14524
14.3%
54512
14.3%
44512
14.3%
34512
14.3%
24512
14.3%
04512
14.3%
64511
14.3%
ValueCountFrequency (%)
04512
14.3%
14524
14.3%
24512
14.3%
34512
14.3%
44512
14.3%
54512
14.3%
64511
14.3%
ValueCountFrequency (%)
64511
14.3%
54512
14.3%
44512
14.3%
34512
14.3%
24512
14.3%
14524
14.3%
04512
14.3%

Hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.49802184
Minimum0
Maximum23
Zeros1317
Zeros (%)4.2%
Memory size247.0 KiB
2022-08-11T19:08:50.682882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.922199035
Coefficient of variation (CV)0.6020339091
Kurtosis-1.204129259
Mean11.49802184
Median Absolute Deviation (MAD)6
Skewness0.0004461886648
Sum363280
Variance47.91683947
MonotocityNot monotonic
2022-08-11T19:08:50.822871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
01317
 
4.2%
11317
 
4.2%
111317
 
4.2%
101317
 
4.2%
91317
 
4.2%
81317
 
4.2%
71317
 
4.2%
61317
 
4.2%
51317
 
4.2%
31317
 
4.2%
Other values (14)18425
58.3%
ValueCountFrequency (%)
01317
4.2%
11317
4.2%
21316
4.2%
31317
4.2%
41317
4.2%
51317
4.2%
61317
4.2%
71317
4.2%
81317
4.2%
91317
4.2%
ValueCountFrequency (%)
231316
4.2%
221316
4.2%
211316
4.2%
201316
4.2%
191316
4.2%
181316
4.2%
171316
4.2%
161316
4.2%
151316
4.2%
141316
4.2%

Date
Date

Distinct31592
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size247.0 KiB
Minimum2019-01-01 00:00:00
Maximum2022-08-09 11:00:00
2022-08-11T19:08:50.980879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:51.168856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2022-08-11T19:08:33.662138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:33.850109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:34.067133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:34.298153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:34.499125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:34.689167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:34.869165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:35.025169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:35.201148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:35.397194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:35.573191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:35.744194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:35.921199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:36.093206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:36.270181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:36.522202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:36.765188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:37.061193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:37.254202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:37.437227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:37.726213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:37.939253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:38.117226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:38.298263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:38.564239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:38.736240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:38.911246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:39.089278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:39.249289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:39.412287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:39.575297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:39.757267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:39.926301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:40.082310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:40.236280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:40.386284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:40.549289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:40.727329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:40.908297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:41.076302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:41.233336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:41.396333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:41.547348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:41.714350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:41.884606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:42.065646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:42.255615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:42.443644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:42.610622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:42.766626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:42.924631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:43.093669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:43.278671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:43.745653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:43.892687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-08-11T19:08:44.044672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-08-11T19:08:51.334912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-11T19:08:51.638873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-11T19:08:52.001875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-11T19:08:52.330887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-08-11T19:08:52.685892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-08-11T19:08:44.348708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-11T19:08:44.800717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-11T19:08:45.093689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-11T19:08:45.232728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateIndexPrevisión diaria D+1 demandaPrevisión diaria D+1 fotovoltaicaPrevisión diaria D+1 eólicaPrecio mercado SPOT DiarioSigno del desvíoSigno_lag_24hSigno_lag_48hHolidayYearMonthDayWeekdayHourDate
02019-01-01 00:00:0023753.00.03214.066.88-1.0NaNNaN1201911102019-01-01 00:00:00
12019-01-01 01:00:0023018.00.03222.066.88-1.0NaNNaN1201911112019-01-01 01:00:00
22019-01-01 02:00:0021808.00.03081.066.00-1.0NaNNaN1201911122019-01-01 02:00:00
32019-01-01 03:00:0020635.00.03069.063.64-1.0NaNNaN1201911132019-01-01 03:00:00
42019-01-01 04:00:0019824.00.02973.058.85-1.0NaNNaN1201911142019-01-01 04:00:00
52019-01-01 05:00:0019544.00.02904.055.471.0NaNNaN1201911152019-01-01 05:00:00
62019-01-01 06:00:0019803.00.02857.056.001.0NaNNaN1201911162019-01-01 06:00:00
72019-01-01 07:00:0020208.00.02669.061.091.0NaNNaN1201911172019-01-01 07:00:00
82019-01-01 08:00:0020076.054.82454.061.01-1.0NaNNaN1201911182019-01-01 08:00:00
92019-01-01 09:00:0020431.0745.62202.061.001.0NaNNaN1201911192019-01-01 09:00:00

Last rows

DateIndexPrevisión diaria D+1 demandaPrevisión diaria D+1 fotovoltaicaPrevisión diaria D+1 eólicaPrecio mercado SPOT DiarioSigno del desvíoSigno_lag_24hSigno_lag_48hHolidayYearMonthDayWeekdayHourDate
315852022-08-09 02:00:0023803.00.06412.0135.57-1.0-1.01.00202289122022-08-09 02:00:00
315862022-08-09 03:00:0023147.30.06176.0130.001.0-1.01.00202289132022-08-09 03:00:00
315872022-08-09 04:00:0022818.80.05936.8129.001.01.0-1.00202289142022-08-09 04:00:00
315882022-08-09 05:00:0023062.00.05801.8131.011.01.0-1.00202289152022-08-09 05:00:00
315892022-08-09 06:00:0024664.30.25609.0143.591.01.01.00202289162022-08-09 06:00:00
315902022-08-09 07:00:0026012.8281.55303.3152.741.01.0-1.00202289172022-08-09 07:00:00
315912022-08-09 08:00:0027288.52516.34604.5152.851.01.01.00202289182022-08-09 08:00:00
315922022-08-09 09:00:0029001.06150.43798.5145.491.01.0-1.00202289192022-08-09 09:00:00
315932022-08-09 10:00:0030403.08451.13253.8140.001.01.01.002022891102022-08-09 10:00:00
315942022-08-09 11:00:0031323.09565.23271.8130.001.01.01.002022891112022-08-09 11:00:00